Application of shunting inhibitory artificial neural networks to medical diagnosis

G. Arulampalam, A. Bouzerdoum

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Citations (Scopus)

Abstract

Shunting inhibitory artificial neural networks (SIANNs) are biologically inspired networks in which the neurons interact among each other via a nonlinear mechanism called shunting inhibition. Since they are high-order networks, SIANNs are capable of producing complex, nonlinear decision boundaries. In this article, feedforward SIANNs are applied to several medical diagnosis problems and the results are compared with those obtained using multilayer perceptrons (MLPs). First, the structure of feedforward SIANNs is presented. Then, these networks are applied to some standard medical classification problems, namely the Pima Indians diabetes and Wisconsin breast cancer classification problems. The SIANN performance compares favourably with that of MLPs. Moreover, some problems with the diabetes data set are addressed and a reduction in the number of inputs is investigated.

Original languageEnglish
Title of host publicationANZIIS 2001 - Proceedings of the 7th Australian and New Zealand Intelligent Information Systems Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages89-94
Number of pages6
ISBN (Electronic)1740520610, 9781740520614
DOIs
Publication statusPublished - 2001
Externally publishedYes
Event7th Australian and New Zealand Intelligent Information Systems Conference, ANZIIS 2001 - Perth, Australia
Duration: 18 Nov 200121 Nov 2001

Publication series

NameANZIIS 2001 - Proceedings of the 7th Australian and New Zealand Intelligent Information Systems Conference

Conference

Conference7th Australian and New Zealand Intelligent Information Systems Conference, ANZIIS 2001
Country/TerritoryAustralia
CityPerth
Period18/11/0121/11/01

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